New car selection in the market using Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method / Mustaqim Ahmad Rosli

Ahmad Rosli, Mustaqim (2020) New car selection in the market using Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method / Mustaqim Ahmad Rosli. [Student Project] (Unpublished)

Abstract

Buying vehicle especially cars in the market for now days is hard decision to make due of changes in various technical and operational parameter specification like body shape, design, technology and many more. Therefore, to overcome from this confusion state some selection procedure techniques are required. In the decision-making model, TOPSIS is one of the selection procedure technique to adopted into this research. This technique provides a base for decision making processes where there are limited numbers of choices but each has large number of attributes. In this project some cars are considered with different attributes and ranked car using TOPSIS technique.

Metadata

Item Type: Student Project
Creators:
CreatorsEmail / ID. Num
Ahmad Rosli, Mustaqim2016734713
Contributors:
ContributionNameID Num. / Email
Thesis advisorNor-Al-Din, Siti MuslihaUNSPECIFIED
Subjects: Q Science > QA Mathematics > Mathematical statistics. Probabilities
Q Science > QA Mathematics > Analysis > Analytical methods used in the solution of physical problems
Q Science > QA Mathematics > Fuzzy logic
Divisions: Universiti Teknologi MARA, Terengganu > Kuala Terengganu Campus > Faculty of Computer and Mathematical Sciences
Programme: Bachelor of Science (Hons) Computational Mathematics
Item ID: 41065
Uncontrolled Keywords: Vehicle ; Cars ; TOPSIS ; Batik Design
URI: http://ir.uitm.edu.my/id/eprint/41065

Download

[img] Text
41065.pdf

Download (133kB)

Fulltext

Fulltext is available at:
  • Kaunter Perkhidmatan Maklumat | Perpustakaan Cendekiawan | Dungun
  • ID Number

    41065

    Others


    View in Google Scholar

    Actions (login required)

    View Item View Item

    Downloads

    Downloads per month over past year